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%matplotlib inline
from IPython.display import HTML,Image,SVG,YouTubeVideo
%matplotlib inline
from IPython.display import HTML,Image,SVG,YouTubeVideo
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from skimage import data
import numpy as np
from skimage.morphology import disk
import skimage.filters.rank as skr
from skimage.measure import label
from skimage.morphology import watershed
from skimage.io import imread
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
from skimage import data
import numpy as np
from skimage.morphology import disk
import skimage.filters.rank as skr
from skimage.measure import label
from skimage.morphology import watershed
from skimage.io import imread
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
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# segment the coins
im = data.coins()
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# segment the coins
im = data.coins()
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# detect the eyes / nose
im = data.chelsea()
plt.imshow(im);
# detect the eyes / nose
im = data.chelsea()
plt.imshow(im);
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# counting the galaxies
im = data.hubble_deep_field()
plt.imshow(im);
# counting the galaxies
im = data.hubble_deep_field()
plt.imshow(im);
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im = data.page()
bg = skr.median(im, disk(10))
res = (1.*im/bg) < .8
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(bg,cmap=plt.cm.gray);
plt.colorbar()
plt.figure()
plt.imshow(res.astype(np.uint8),cmap=plt.cm.gray);
plt.colorbar();
im = data.page()
bg = skr.median(im, disk(10))
res = (1.*im/bg) < .8
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(bg,cmap=plt.cm.gray);
plt.colorbar()
plt.figure()
plt.imshow(res.astype(np.uint8),cmap=plt.cm.gray);
plt.colorbar();
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# segment the cells
im = imread('../data/dh_phase.png')
th = im>150
th1 = im>100
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(1.*th+th1,cmap=plt.cm.gray)
plt.colorbar();
# segment the cells
im = imread('../data/dh_phase.png')
th = im>150
th1 = im>100
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(1.*th+th1,cmap=plt.cm.gray)
plt.colorbar();
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from skimage.feature import canny
ca = canny(im)
plt.figure(figsize=[10,10])
plt.imshow(ca,cmap=plt.cm.gray);
from skimage.feature import canny
ca = canny(im)
plt.figure(figsize=[10,10])
plt.imshow(ca,cmap=plt.cm.gray);
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from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
lab,n_lab = label(th,return_num=True)
bg = th1==0
lab[bg] = n_lab+1
#med = skr.median(im,disk(5))
#gr = skr.gradient(med,disk(3))
ws = watershed(255-im,lab)
plt.imshow(mark_boundaries(im,ws))
from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
lab,n_lab = label(th,return_num=True)
bg = th1==0
lab[bg] = n_lab+1
#med = skr.median(im,disk(5))
#gr = skr.gradient(med,disk(3))
ws = watershed(255-im,lab)
plt.imshow(mark_boundaries(im,ws))
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
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<matplotlib.image.AxesImage at 0x7fc8e471a550>
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im = imread('../data/exp0001.jpg')
plt.figure(figsize=[20,20])
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
im = imread('../data/exp0001.jpg')
plt.figure(figsize=[20,20])
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# count red and yellow flowers
im = imread('../data/flowers.jpg')
plt.imshow(im)
plt.colorbar();
# count red and yellow flowers
im = imread('../data/flowers.jpg')
plt.imshow(im)
plt.colorbar();
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# find the fiber orientation
im = imread('../data/image4.png')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# find the fiber orientation
im = imread('../data/image4.png')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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from IPython.display import YouTubeVideo
YouTubeVideo('PUcz11MLxUk', start=0, autoplay=1, theme="light", color="blue",)
from IPython.display import YouTubeVideo
YouTubeVideo('PUcz11MLxUk', start=0, autoplay=1, theme="light", color="blue",)
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# detect stroma
im = imread('../data/Rp042826d.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# detect stroma
im = imread('../data/Rp042826d.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# segment the flowers
im = imread('../data/KaneFlowers.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# segment the flowers
im = imread('../data/KaneFlowers.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
gr = skr.gradient(im,disk(3))
local_min = im <= skr.minimum(im,disk(5))
lab = label(local_min)
#med = skr.median(im,disk(5))
ws = watershed(gr,lab)
plt.figure(figsize=[10,10])
plt.imshow(mark_boundaries(im,ws))
#plt.imshow(local_min)
from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
gr = skr.gradient(im,disk(3))
local_min = im <= skr.minimum(im,disk(5))
lab = label(local_min)
#med = skr.median(im,disk(5))
ws = watershed(gr,lab)
plt.figure(figsize=[10,10])
plt.imshow(mark_boundaries(im,ws))
#plt.imshow(local_min)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
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<matplotlib.image.AxesImage at 0x7fc8e41d1910>
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rgb = imread('../data/4colors.JPG')
plt.figure(figsize=[20,20])
plt.imshow(rgb)
plt.colorbar();
rgb = imread('../data/4colors.JPG')
plt.figure(figsize=[20,20])
plt.imshow(rgb)
plt.colorbar();
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r = skr.median(rgb[:,:,0],disk(1))
plt.imshow(r,cmap=plt.cm.gray)
r = skr.median(rgb[:,:,0],disk(1))
plt.imshow(r,cmap=plt.cm.gray)
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<matplotlib.image.AxesImage at 0x7fc8e41104d0>
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s = rgb.sum(axis=2)
th = s > 100
#post-processing
pth = skr.minimum(th.astype(np.uint8),disk(1))
plt.figure(figsize=[20,20])
plt.imshow(pth,cmap=plt.cm.gray)
plt.colorbar()
s = rgb.sum(axis=2)
th = s > 100
#post-processing
pth = skr.minimum(th.astype(np.uint8),disk(1))
plt.figure(figsize=[20,20])
plt.imshow(pth,cmap=plt.cm.gray)
plt.colorbar()
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<matplotlib.colorbar.Colorbar at 0x7fc8e42ac990>
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lab = label(pth)
lut = np.arange(0,np.max(lab)+1)
plt.imshow(lab)
plt.colorbar()
mask = lab == 20
plt.imshow(mask)
lab = label(pth)
lut = np.arange(0,np.max(lab)+1)
plt.imshow(lab)
plt.colorbar()
mask = lab == 20
plt.imshow(mask)
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<matplotlib.image.AxesImage at 0x7fc8e60d36d0>
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from random import shuffle
shuffle(lut)
from random import shuffle
shuffle(lut)
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shuffle(lut)
plt.imshow(lut[lab])
plt.colorbar()
shuffle(lut)
plt.imshow(lut[lab])
plt.colorbar()
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<matplotlib.colorbar.Colorbar at 0x7fc8e4791c10>
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# segment the cell
im = imread('../data/exp0001crop.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# segment the cell
im = imread('../data/exp0001crop.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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m = skr.median(im,disk(5))
plt.imshow(m,cmap=plt.cm.gray)
plt.colorbar()
m = skr.median(im,disk(5))
plt.imshow(m,cmap=plt.cm.gray)
plt.colorbar()
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<matplotlib.colorbar.Colorbar at 0x7fc8e44f5b90>
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th1 = m < 90
th2 = np.bitwise_and(110 > m,m < 130)
plt.imshow(th2)
th1 = m < 90
th2 = np.bitwise_and(110 > m,m < 130)
plt.imshow(th2)
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<matplotlib.image.AxesImage at 0x7fc8e424dc50>
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markers = label(th2)
plt.imshow(markers)
plt.colorbar()
markers = label(th2)
plt.imshow(markers)
plt.colorbar()
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<matplotlib.colorbar.Colorbar at 0x7fc8e4625510>
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markers[markers==3] = 2
ws = watershed(im,markers)
markers[markers==3] = 2
ws = watershed(im,markers)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
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plt.imshow(ws)
plt.imshow(mark_boundaries(im,ws))
plt.imshow(ws)
plt.imshow(mark_boundaries(im,ws))
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<matplotlib.image.AxesImage at 0x7fc8e46d0b10>
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# segment the cell
im = imread('../data/brain.jpg')[:,:,0]
plt.figure(figsize=(10,10))
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# segment the cell
im = imread('../data/brain.jpg')[:,:,0]
plt.figure(figsize=(10,10))
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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plt.hist(im.flatten(),255);
plt.hist(im.flatten(),255);
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from skimage.filters import threshold_otsu
t_otsu = threshold_otsu(im)
t_otsu
from skimage.filters import threshold_otsu
t_otsu = threshold_otsu(im)
t_otsu
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36
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th = im > t_otsu
plt.figure(figsize=(10,10))
plt.imshow(th)
th = im > t_otsu
plt.figure(figsize=(10,10))
plt.imshow(th)
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<matplotlib.image.AxesImage at 0x7fc8e425dd90>
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lab = label(th,connectivity=1)
plt.imshow(lab)
lab = label(th,connectivity=1)
plt.imshow(lab)
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<matplotlib.image.AxesImage at 0x7fc8dfdc2ed0>
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from skimage.measure import regionprops
from skimage.measure import regionprops
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props = regionprops(lab)
brain = (lab==7).astype(np.uint8)
pp = skr.maximum(brain,disk(3))
pp = skr.minimum(pp,disk(3))
plt.imshow(pp)
props = regionprops(lab)
brain = (lab==7).astype(np.uint8)
pp = skr.maximum(brain,disk(3))
pp = skr.minimum(pp,disk(3))
plt.imshow(pp)
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<matplotlib.image.AxesImage at 0x7fc8dfd43ed0>
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for p in props:
print(p.area, p.label)
for p in props:
print(p.area, p.label)
1459 1 5 2 1 3 3 4 1 5 16 6 6323 7 1 8 2 9 1 10 1 11 1 12 1 13 16 14 1 15 1 16 1 17 2 18 2 19 2 20 2 21 30 22 1 23 1 24 1 25 1 26 2 27 2 28 5 29 1 30 1 31 2 32 1 33 13 34
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